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This book highlights the latest advances and trends in advanced
signal processing (such as wavelet theory, time-frequency analysis,
empirical mode decomposition, compressive sensing and sparse
representation, and stochastic resonance) for structural health
monitoring (SHM). Its primary focus is on the utilization of
advanced signal processing techniques to help monitor the health
status of critical structures and machines encountered in our daily
lives: wind turbines, gas turbines, machine tools, etc. As such, it
offers a key reference guide for researchers, graduate students,
and industry professionals who work in the field of SHM.
This book highlights the latest advances and trends in advanced
signal processing (such as wavelet theory, time-frequency analysis,
empirical mode decomposition, compressive sensing and sparse
representation, and stochastic resonance) for structural health
monitoring (SHM). Its primary focus is on the utilization of
advanced signal processing techniques to help monitor the health
status of critical structures and machines encountered in our daily
lives: wind turbines, gas turbines, machine tools, etc. As such, it
offers a key reference guide for researchers, graduate students,
and industry professionals who work in the field of SHM.
Wavelets: Theory and Applications for Manufacturing presents a
systematic description of the fundamentals of wavelet transform and
its applications. Given the widespread utilization of rotating
machines in modern manufacturing and the increasing need for
condition-based, as opposed to fix-interval, intelligent
maintenance to minimize machine down time and ensure reliable
production, it is of critical importance to advance the science
base of signal processing in manufacturing. This volume also deals
with condition monitoring and health diagnosis of rotating machine
components and systems, such as bearings, spindles, and gearboxes,
while also: -Providing a comprehensive survey on wavelets
specifically related to problems encountered in manufacturing
-Discussing the integration of wavelet transforms with other soft
computing techniques such as fuzzy logic, for machine defect and
severity classification -Showing how to custom design wavelets for
improved performance in signal analysis Focusing on wavelet
transform as a tool specifically applied and designed for
applications in manufacturing, Wavelets: Theory and Applications
for Manufacturing presents material appropriate for both academic
researchers and practicing engineers working in the field of
manufacturing.
Wavelets: Theory and Applications for Manufacturing presents a
systematic description of the fundamentals of wavelet transform and
its applications. Given the widespread utilization of rotating
machines in modern manufacturing and the increasing need for
condition-based, as opposed to fix-interval, intelligent
maintenance to minimize machine down time and ensure reliable
production, it is of critical importance to advance the science
base of signal processing in manufacturing. This volume also deals
with condition monitoring and health diagnosis of rotating machine
components and systems, such as bearings, spindles, and gearboxes,
while also: -Providing a comprehensive survey on wavelets
specifically related to problems encountered in manufacturing
-Discussing the integration of wavelet transforms with other soft
computing techniques such as fuzzy logic, for machine defect and
severity classification -Showing how to custom design wavelets for
improved performance in signal analysis Focusing on wavelet
transform as a tool specifically applied and designed for
applications in manufacturing, Wavelets: Theory and Applications
for Manufacturing presents material appropriate for both academic
researchers and practicing engineers working in the field of
manufacturing.
Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis
introduces the theory and latest applications of transfer learning
on rotary machine fault diagnosis and prognosis. Transfer
learning-based rotary machine fault diagnosis is a relatively new
subject, and this innovative book synthesizes recent advances from
academia and industry to provide systematic guidance. Basic
principles are described before key questions are answered,
including the applicability of transfer learning to rotary machine
fault diagnosis and prognosis, technical details of models, and an
introduction to deep transfer learning. Case studies for every
method are provided, helping readers apply the techniques described
in their own work.
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